Discriminating Analysis of Metal Ions via Multivariate Curve Resolution-Alternating Least Squares Applied to Silver Nanoparticle Sensor.

Heavy metals are life-threatening pollutions because of their great toxicity, long-term persistence in nature and their bioaccumulation in living organisms. In this work, we performed multivariate curve resolution-alternating least squares analysis of UV-Vis raw spectra received by a colorimetric sensor constructed on mercaptoundecanoic acid functionalized silver nanoparticles (AgNPs@11MUA) to detect Cd, Cu, Mn, Ni, and Zn in water. This combined approach allowed the rapid identification and quantification of multiple heavy metals and showed adequate sensitivity and selectivity, thus representing a promising analytical and computational method for both laboratory and field applications such as environmental safety and public health monitoring.

Development of a one-pot synthesis of rGO in water by optimizing Tour's method parameters.

Since its first synthesis in 2004, graphene has been widely studied and several different synthesis methods has been developed. Solvent exfoliation of graphite and the reduction of graphene oxide previously obtained through graphite oxidation are the most employed. In this work, we exploited synthesis conditions of a method usually employed for obtaining graphene oxide (the Tour's method) for directly obtaining a very poorly oxidised material with characteristics like reduced graphene oxide.

Rapid LA-REIMS-based metabolic fingerprinting of serum discriminates aflatoxin-exposed from non-exposed pregnant women: a prospective cohort from the Butajira Nutrition, Mental Health, and Pregnancy (BUNMAP) Study in rural Ethiopia.

To date, the changes in maternal metabolic response associated with prenatal aflatoxin exposure remain largely unknown. This study investigated the effects of prenatal aflatoxin exposure on the maternal serum metabolome in rural Ethiopia. A total of 309 pregnant women were enrolled prospectively, and their serum aflatoxin concentrations were measured using targeted liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS).

An initial exploration of machine learning for establishing associations between genetic markers and THC levels in Cannabis sativa samples.

Cannabis sativa, a globally commercialized plant used for medicinal, food, fiber production, and recreation, necessitates effective identification to distinguish legal and illegal varieties in forensic contexts. This research utilizes multivariate statistical models and Machine Learning approaches to establish correlations between specific genotypes and tetrahydrocannabinol (Δ-THC) content (%) in C. sativa samples.